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Use of large medicinal databases to study associations between diseases Quarterly Journal of Medicine, 93, 669 675 Gomatam, S, Carter, R, Ariet, M and Mitchell, G (2002) An empirical comparison of record linkage procedures Statistics in Medicine, 21, 1485 1496 Hattersley, L and Boyle, P (2007) The Scottish Longitudinal Study: an introduction LSCS Working Paper 10 St Andrews: Longitudinal Studies Centre Scotland Herriot, R, Bowie, C, Kasprzyk, D and Haber, S (1988) Enhanced demographic economic data sets Survey of Income and Program Participation Working Paper, No 82, US Department of Commerce Bureau of the Census Herzog, T N, Scheuren, F J and Winkler, W E (2007) Data Quality and Record Linkage Techniques New York: Springer Verlag..





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where S(0) is the spot level at time 0 (when we calibrate the model). The CEV in (7.20) is characterized by two parameters: the ATM local volatility 0 and the skew parameter . The estimates of that produce the observed implied smile curve in the S&P 500 option market are around 4. The model with 0 < < 1 is called restricted CEV since the gamma is not enough to t the observed smile. The model with = 1 is the standard Black-Scholes lognormal process. Models with < 0 are called unrestricted CEV. They t the smile rather well. The problem, however, is that for the unrestricted CEV, the volatility goes to in nity as the asset price goes to 0. This is an undesirable feature of the model that can be handled by introducing a cutoff point that prevents the volatility from blowing up. The restricted CEV PDE is actually analytically tractable and can be expressed in terms of hypergeometric (Bessel) functions as shown by Cox and Ross (1976). However, while there are alternative parametric choices that allow the model to t the observed smile for a given maturity date, such parameterizations do not t the model to the term structure. Alternative speci cations of the volatility are needed. It is important to note that with the CEV model, the variance of the asset rate of return depends on the price of the underlying asset, and/or time as well. This is a different framework than the Black-Scholes, which assumes constant variance and independence of changes in asset returns on the asset s price level. The Black-Scholes is actually a special case of the CEV model when = 1. Cox and Rubinstein (1985) provide a closed-form analytic formula for pricing a European call with CEV: C=S

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recommended for its computational ef ciency, leading to more than real-time performances. In particular, the ideas above have been applied to face tracking by Matthews and Baker [116]. Subsequently, appearance can be estimated using eq. (4.119), and unlike the pose estimation, it is a linear problem in the respective parameter a, which can be solved in one step. However, to solve the entire problem in only two steps, without need of further iterations, we need to reparametrize the problem by means of the appearance subspace projection method described below. By considering the complete minimization problem, we can write s* = arg min

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